Avoid Overfitting Using Regularization in TensorFlow

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In this Guided Project, you will:

Develop an understanding on how to avoid over-fitting with weight regularization and dropout regularization

Be able to apply both weight regularization and dropout regularization in Keras with TensorFlow backend

Clock2 hours
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

In this 2-hour long project-based course, you will learn the basics of using weight regularization and dropout regularization to reduce over-fitting in an image classification problem. By the end of this project, you will have created, trained, and evaluated a Neural Network model that, after the training and regularization, will predict image classes of input examples with similar accuracy for both training and validation sets. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Skills you will develop

  • Data Science
  • Deep Learning
  • Machine Learning
  • Tensorflow
  • keras

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Import the data

  2. Process the data

  3. Regularization and Dropout

  4. Creating the Experiment

  5. Assess the final results

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step



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